Theoretical and Applied Climatology最新文献

Soil moisture plays a critical role in agricultural productivity and water resource management, especially in a diverse and populous country like India. Understanding variations in soil moisture across different regions and seasons is essential for adapting agricultural practices and water management strategies to local conditions. This study examines changes in soil moisture levels across India, comparing contemporary data from 2023 with historical averages from 2000 to 2005 using advanced remote sensing and GIS technologies. The primary objective of this study is to identify Soil Moisture Anomalies (SMA) across India, quantify their impacts on agriculture and water resources, and provide recommendations for targeted management strategies. By comparing recent soil moisture data against historical averages, the study aims to highlight trends and changes that could influence future water resource planning and agricultural practices. The research utilizes data from the Famine Early Warning Systems Network’s (FEWS NET) i.e. Land Data Assimilation System (FLDAS), obtained from NASA’s data archives. The study employs a systematic approach to analyze seasonal variations in soil moisture across different Indian states. Soil moisture levels were analyzed using zonal statistics in GIS to classify regions into categories based on the degree of anomaly observed. This classification helped in understanding the spatial distribution of soil moisture during the pre-monsoon, monsoon, and post-monsoon seasons. The study found significant regional and seasonal variations in soil moisture across India. During the monsoon period, areas such as Bihar and Jharkhand consistently showed significant moisture deficits, indicative of drought conditions, affecting agricultural output and necessitating urgent water conservation measures. Conversely, regions like Punjab benefited from positive soil moisture anomalies, enhancing agricultural productivity. The pre-monsoon and post-monsoon seasons also showed variations, with some areas experiencing deficits requiring careful water management while others had surpluses that increased the risk of flooding. The analysis of SMA in India underscores the need for region-specific agricultural and water management strategies that consider significant variability in soil moisture conditions. The study highlights the importance of integrating soil moisture monitoring into national policy frameworks to enhance climate resilience and sustainable agricultural practices. Future research should focus on updating soil moisture assessments with more recent data and refining predictive models to improve the accuracy and effectiveness of water management and agricultural interventions.

Climate change is expected to intensify compound dry and hot events (CDHEs) in China, exacerbating socioeconomic exposure to CDHEs. Based on 23 global climate models (GCMs) data from Coupled Model Intercomparison Project 6 (CMIP6), this study analyzes and projects the socioeconomic exposure to CDHEs and its influencing factors under 1.5℃ and 2.0℃ global warming levels. The results show that the frequency of CDHEs is expected to be higher under 2.0℃ warming levels than that under 1.5℃ warming levels. Population exposures to CDHEs are projected to increase by 160 × 10⁶ persons-months (about 280%) and 210 × 10⁶ persons-months (310%) under 1.5℃ and 2.0℃ warming levels, respectively. The region with the highest increase in population exposure to CDHEs is East China, followed by Central China and South China; and the regions with the smallest increase in population exposure are Tibet, Inner Mongolia, and Xinjiang. GDP exposures are expected to increase by 24 times and 20 times under 1.5 °C warming levels for SSP2-4.5 and SSP5-8.5 scenarios, while the values would be up to 38 times and 28 times under 2.0 °C warming levels. The climate effect (accounting for 80%) is the determinate factor that triggers the change of population exposure to CDHEs, followed by the interaction between the population and climate changes, while the influence of the population factor is the least. Interactive effect contributes the most to GDP exposure whereas climate change contributes the least. Across most regions of China, the warming level is the main uncertainty source, accounting for 46.1% and 70.5% of the population and GDP exposure, respectively. The results are beneficial for identifying hotspots of vulnerable regions exposed to CDHEs and provide beneficial information for conducting climate change mitigation and adaptation strategies.

Regional studies are crucial for monitoring and managing the impacts of extreme climatic events. This phenomenon is particularly important in some areas, such as the Mediterranean region, which has been identified as one of the most responsive regions to climate change. In this regard, the analysis of large space-time sets of climatic data can provide potentially valuable information, although the datasets are commonly affected by the issue of missing data. This approach can significantly reduce the reliability of inferences derived from space-time data analysis. Consequently, the selection of an effective missing data recovery method is crucial since a poor dataset reconstruction could lead to misleading the decision makers’ judgments. In the present paper, a methodology that can enhance the confidence of the statistical analysis performed on the reconstructed data is presented. The basic assumption of the proposed methodology is that missing data within certain percentages cannot significantly change the shape or parameters of the complete data distribution. Therefore, by applying several missing data recovery methods whose reconstructed dataset better overlaps the original dataset, larger confidence is needed. After the gap filling procedure, the temporal tendencies of the annual daily minimum temperature (T < 0 °C) were analysed in the Calabria region (southern Italy) by applying a test for trend detection to 8 temperature series over a 30-year period (1990–2019). The results showed that there was a constant reduction in the duration of frosty days, indicating the reliability of the effect of climate change.

Droughts pose serious threats to the agricultural sector, especially in rainfed-dominated agricultural regions like those in Argentina’s Humid Pampas. This region was recently impacted by slow-evolving and long-lasting droughts as well as by flash droughts, resulting in losses reaching thousands of millions of US dollars. Improvements of drought early warning systems are essential, particularly given the projected increase in drought frequency and severity over southern South America. The spatial and temporal relationship between precipitation deficits, soil moisture and vegetation health anomalies are crucial for better understanding and representation of the agricultural droughts and their impacts. In this context, the Combined Drought Indicator (CDI) considers the causal and time-lagged relationship of these three variables. The study’s objective is twofold: (1) Analyze the time-lagged response between precipitation deficits, soil moisture and satellite fAPAR anomalies; and (2) Evaluate the CDI’s capability to characterize the severity of drought events on the Humid Pampas against agricultural yield estimations and simulations, as well as agricultural emergency declarations. The correlation among the variables shows strong spatial variability. The highest Pearson correlation values (r > 0.42) are observed over parts of the Humid Pampas for time lags of 0, 10, and 20 days between the variables. Although the CDI has limitations, such as its coarse spatial resolution and monthly temporal resolution of precipitation data, it effectively tracks the progression of major drought events in the region. The CDI’s performance aligns well with estimations and simulations of soybean and corn yields, as well as official declarations of agricultural emergencies. Insights from this study also provide a basis for discussing potential improvements to the CDI. This study highlights the global and regional significance of evaluating and enhancing the CDI for effective drought monitoring, emphasizing the role of collaborative efforts for future advancements in drought early warning systems.

The Global Navigation Satellite System Reflectometry (GNSS-R) has emerged as a valuable tool for terrestrial remote sensing applications, particularly in the context of land Surface Soil Moisture (SSM) detection. The high-resolution capability of GNSS-R complements traditional satellite-based active and passive missions but the product reliability and robustness evaluations are still absent due to an efficient retrieval algorithms. In this study, we addressed this lack of reliability and robustness by comprehensively assessing the SSM retrievals from CYclone Global Navigation Satellite System (CYGNSS) data with the satellite-based microwave radiometry products Soil Moisture Active Passive (SMAP) and Modern Era Retrospective-Analysis for Research and Applications (MERRA2) over Punjab in various seasons. ERA5 model-based products for the same period in 2022–2023. Our study reveals a distinct seasonal average SSM variation during autumn (0.20 cm³/cm³), followed by winter values of 0.19 cm³/cm³. Subsequently, the minimum SSM values are observed during summer (0.11 cm³/cm³) and an increase in spring to 0.13 cm³/cm³. Moreover, a strong positive linear relationship (0.74) is evident between SMAP and ERRA 5 in contrast to a low correlation (0.03) between MERRA2 and both the SMAP and ERRA 5. Additionally, SMAP demonstrates moderate and weak correlation of 0.53 and 0.03 with CYGNSS and MERRA2, respectively. The CYGNSS exhibits moderate correlations (0.46) with ERRA 5 and SMAP and a weaker association (0.14) with MERRA2. Our analysis concluded that MERRA2 (Bias = 0.20 cm³/cm³, ubRMSD = 0.25 cm³/cm³, RMSE = 0.12 cm³/cm³, SD = 0.13 cm³/cm³, MAE = 0.04 cm³/cm, R = 0.03) SSM product performs poorly as compared to SMAP (Bias = 0.03 cm³/cm³, ubRMSD = 0.03 cm³/cm³, RMSE = 0.04 cm³/cm³, SD = 0.05 cm³/cm³, MAE = 0.03 cm³/cm³, R = 0.74) and CYGNSS (Bias = -0.01 cm³/cm³, ubRMSD = 0.09 cm³/cm³, RMSE = 0.07 cm³/cm³, SD = 0.06 cm³/cm³, MAE = 0.05 cm³/cm³, R = 0.46) products. This study provides accurate future predictions of SSM with delineating the limitations of GNSS-R in comparison to remote sensing and model values. The findings from this study have also significant implications for the advancement of GNSS-R applications in agriculture and crop management.

This study conducts a comprehensive analysis of the influence of tropical cyclones on precipitation variations in Indochina, examining Vietnam, Laos, and Cambodia, while exploring their connection with evolving climatic variables. Covering a span of four decades (1979–2021) and integrating daily precipitation records with climatic datasets, the research elucidates tropical cyclone’s contributions to the annual precipitation across distinct regions, revealing percentages of 27%, 16%, and 6% in Vietnam, Laos, and Cambodia, respectively. Spatial distribution mapping highlights concentrated intensities in central Vietnam, central Laos, and southern Cambodia. Additionally, an upward trend in Vietnam’s precipitation, as a representative measure of the entire region, is observed over the study duration, while its variability exhibits marginal correlations with inter-annual and decadal-scale climatic indices. The upward trend aligns with increased precipitable water over Indochina and open oceans, increased sea surface temperatures, reinforced atmospheric low-pressure systems, and intensified westerly wind patterns post-2000. These findings underscore the complex interplay between climate variables and Indochina’s precipitation dynamics, suggesting implications for disaster management and strategies to adapt to climate change.

Analysis of low river flows provides important information for effective management of water resources in a region. Despite the critical importance of understanding low flow dynamics, there is a gap in the literature regarding the use of non-stationary models to analyze low flow data under climate change in Turkey. In this research, low flow series from 80 measuring stations in Turkey are investigated by employing both stationary and non-stationary models based on the Generalized Additive Models for Location, Scale and Shape (GAMLSS). For constructing non-stationary models, 31 explanatory variables consisting of time, precipitation, temperature and atmospheric oscillation indices were used to model the parameters of the chosen distributions. The results show that stationary models are more successful at 7 stations, while non-stationary models are more successful at 73 stations. Comparisons between non-stationary models showed that for most stations, the best performing models were non-stationary models with annual precipitation as covariates. In addition, successful results were obtained when Western Mediterranean Oscillation and North Atlantic Oscillation indices were used as explanatory variables. Additionally, this study investigated 20 and 50-year return levels by fitting the non-stationary frequency distribution models for low flows over historical and projection periods under SSP2-4.5 and SSP5-8.5 climate scenarios. GAMLSS incorporated annual total precipitation, which is the most effective explanatory variable for low flows, as a covariate, and thus changes in low flows were analyzed. The results show that decreases are expected in low flows, except for the stations in the upper Euphrates basin compared to the historical period.

With the advent of numerous global precipitation estimates (GPEs) in the recent decades, dependability of hydrologists has lessened on the station data as the GPEs can be readily availed and utilized. Since the skills of GPEs may differ from region-to-region, it is vital to analyse their ability in resolving the regional precipitation climatology using appropriate statistical methods. In this study, a total of five GPEs, viz., APHRODITE, PERSIANN-CDR, CHIRPS, CMORPH, and IMERG were evaluated for their abilities in resolving regional precipitation climatology of WHR with respect to gridded precipitation product of India Meteorological Department (IMD). Different performance indicators i.e., Probability of Detection (POD), False Alarm Ratio (FAR), Normalised Root Mean Square Deviation (NRMSD), Pearson Correlation Coefficient (CC) and Skill Score (SS) were used for evaluating the GPEs. Multicriterion Decision Making (MCDM)approaches i.e., Compromise Programming (CP), Cooperative Game Theory (CGT), Technique for Order of Preference by Similarity to Ideal Solution (TOPSIS), Weighted Average Technique (WAT), and Fuzzy TOPSIS were used for ranking the GPEs across different grids in WHR. Entropy based weight assignment to NRMSD, CC, and SS were performed while applying them in MCDM methods. Group Decision Making (GDM) approach utilizing spearman correlation coefficient and additive ranking rule was employed to obtain the final ranking of GPEs from multiple rankings assigned through different MCDM methods. Across 115 grids, APHRODITE exhibits superior performance compared to other GPEs in 89 grids. Conversely, CHIRPS and CMORPH emerge as the least favorable products among the five GPEs across more than 70 grids, being consistently ranked either 4th or 5th. Notably, IMERG was identified as the best-performing product in 14 grids and as the second-best product in 63 grids, positioning it as the second most suitable option after APHRODITE for monthly rainfall time series analysis. Similar results, as detailed in the paper, were also obtained for month-wise rainfall time series analysis.

Understanding trends in hydroclimatic variables is crucial for linking local climatic drivers with regional water use practices, particularly in a vulnerable Haor basin in tropical country like Bangladesh. This study evaluated the spatiotemporal trends in hydroclimatic variables at annual and seasonal scales using advanced statistical methods, including the Modified Mann–Kendall (MK) test, Sen’s slope, Sequential Mann-Kendal, Pettitt test, and linear regression model. Additionally, Detrended Fluctuation Analysis (DFA) and Morlet Wavelet Analysis (MWA) were utilized to analyze historical periodic cycles and predict future trends. Results show a significant decrease in annual and seasonal surface water levels (SWL) and rainfall, except for the monsoon, while both maximum and minimum temperatures simultaneously increased. The decline in annual SWL at a rate of 1.18 m/year was influenced by an increase in maximum temperature at a rate of 0.03 °C/year and a decrease in annual total rainfall at a rate of 5.25 mm/year. DFA analysis suggests long-term correlations among these variables, predicting future increases in temperature but continued decreases in rainfall and SWL. Periodic cycles with various frequencies were observed in rainfall, maximum, and minimum temperatures. ECMWF ERA5 reanalysis datasets attribute these changes to higher pre-monsoon geopotential heights, lower relative humidity, and higher monsoon rainfall associated with lower surface pressure. The findings of the study will help develop targeted climate adaptation strategies to mitigate the adverse effects on agriculture, biodiversity, and freshwater availability in the region. The overall study provides essential data that can inform water resource management strategies.

Faced with the challenges presented by climate change, the necessity to navigate the sustainable development of China’s skiing industry emerges as a pivotal and pressing concern, especially considering the region’s vulnerability to climate variations and its burgeoning status as an emerging skiing destination. This study develops a methodology to assess the impact of climate change on ski resorts that is especially applicable in situations with limited climate station data and can be employed by ski industry stakeholders. A multiple linear regression (MLR) based on climate parameters from 1981 to 2010 is coupled with climate change projections under RCP4.5 and RCP8.5 scenarios for the 2020s, 2050s, and 2080s. To validate the precision of the MLR model assessment, the study compares the results with those of the SkiSim 2.0 model — a model widely applied in various countries and regions for evaluating the impact of climate change on the ski industry. Results from the MLR model reveal that there are comparatively modest decreases in skiing days in the northeast and northwest regions, contrasting with significant declines in the eastern, central, and southwestern areas. The findings of the MLR model are largely consistent with SkiSim 2.0, thereby broadly validating this approach. A series of implications and recommendations for further studies and industry applications are provided.